We must move away from ‘cookie-cutter’ chatbots

Bad chatbots don’t sit well with customers, but ‘intelligent’ ones can be highly effective.

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It’s no secret that when chatbots are bad, they’re really bad. There’s nothing worse than trying to convey your needs and problems in a chat box, only to be met with irrelevant automated responses.

Even entities such as CNN and the Wall Street Journal have struggled to build bots that could understand simple unsubscribe requests from their users. CNN’s bot only understood the request when the word “unsubscribe” was sent on its own, while WSJ’s bot just ignored the request altogether, and continued to bombard its users.

Customer interactions with rudimentary chatbots can be slow, intrusive, and misleading, and are enough to drive visitors away for good. In fact, studies show that 73 percent of users would not use a chatbot again if they perceived the experience to be negative. A supposedly helpful tool could end up driving revenue away from your business— yet many companies still choose to deploy these clunky, generic tools.

However, with the advent of artificial intelligence (AI) and natural language understanding (NLU) technologies, custom chatbots are able to interact with site visitors in a much more informative and fluid way, providing contextual information and guiding users to their pre-set goals.

Let’s take a look at why companies need to scrap many of the current chatbots and invest in ones that are enhanced with these new technologies.

Where is the traditional bot going wrong?

We’re all familiar with the feeling of getting nowhere with automated responses, only to end up needing to speak to a human customer service representative after wasting 20 minutes of our time with a chatbot. This usually happens with rule-based chatbots that follow strict conversation paths, leading to dead ends where the chatbots doesn’t have any appropriate responses for the questions asked.

Traditional chatbots are often made in a cookie-cutter fashion for any website. For example, a chatbot that’s been deployed on a retail site may be the same one being used on a news site, and thus answer questions in the same fashion without any domain knowledge of either website.

In fact, if the information the user is seeking to find out is simple enough to be given out by a rule-based chatbot, there’s probably a more efficient way to convey that information. And there’s no need to create chatbots to solve problems that don’t require a back-and-forth conversation, like finding out opening hours or buying tickets, for example.

Not to mention, chatbots without NLU can only recognize keywords and act on them, and often give customers the option to choose from a long list of buttons to move the interaction along. All of this significantly decreases the amount of time any user wants to spend interacting with a chatbot— and thus the amount of time they spend engaging with your business.

What difference do AI and NLU make?

The contrast between traditional chatbots and those enhanced with AI and NLU is stark and can be the difference between a customer having an informative, free-flowing conversation that swiftly solves their problem, and them being put off by irrelevant questions and unnatural dialogue.

According to Gartner, AI “appears to emulate human performance typically by learning, coming to its own conclusions [and by] appearing to understand complex content.” This makes AI-enhanced chatbots much more conducive to natural dialogue. Not only this, but AI chatbots improve continuously because of the amount of data they collect— and learn from— over time.

As the chatbot has more conversations with site visitors, it evolves and tweaks its responses. If the AI’s response is incorrect, and then the agent changes the answer to better suit the customer’s needs, it will learn from the exchange and will answer more appropriately the next time it’s asked a similar question.

This deep learning model works in tandem with the specific domain knowledge that the chatbot now has. Now able to understand the context of the questions asked, the chatbot can give meaningful and appropriate answers.

And with NLU, the customer no longer has to intentionally speak like they’re interacting with a machine. Users of NLU-enhanced chatbots can converse via chat as they would with a friend. Technology also has the ability to categorize customer data by tagging parts of speech and reformatting numbers and dates so the machine can read them. It’s the chatbot’s job to understand the normal speech of the customer and take action accordingly.

NLU capabilities also allow the chatbot to understand customer preferences, feelings, and inclinations, resulting in in-depth insights that deepen customer relationships. For example, it could detect when a customer is becoming irate and prompt a human operator to take over the chat.

How does this help drive revenue and results?

Having capable chatbots on a website means that business owners can actually rely on them to have meaningful and contextual conversations with site visitors. These conversations result in engaged users with their expectations exceeded— ultimately increasing the likelihood of them converting into buyers.

By having knowledge of the site, accuracy is significantly increased. For example, for a retail site, a chatbot would have detailed knowledge of the items being sold on the site and can recommend appropriate products that go well with what they are looking for.

For example, ‘RecipeBots’ have recently been developed that answer questions and recommend recipes that are best suited for the user’s needs. Once the user selects a recipe, the RecipeBot adds the ingredients into a shopping cart with a single click. With this one click, the user can have all of the required products shipped to their home for them to start making their dish of choice. Businesses hosting the RecipeBot increase sales by helping users find and buy the ingredients, while site-users benefit from significantly decreasing the time and effort it takes to buy everything they need for a certain recipe.

Satisfied customers mean loyal customers, which mean higher revenues. Add to this the money saved on HR costs by employing AI to deal with what would usually go through human customer service representatives. And by taking care of repetitive tasks, AI chatbots enable human employees to spend more of their time on more interesting and stimulating work.

How can chatbots help a business run smarter?

So, for companies that are keen to deepen customer relationships and increase revenue, AI and NLU-enhanced chatbots seem to be a no-brainer. In fact, around 40 percent of large businesses have already implemented or are in the process of implementing intelligent assistants or AI chatbots, compared to 25-27 percent of SMEs.

The possibilities for chatbots to drive other, unexpected benefits for a business is high. Richard Socher, Salesforce’s chief scientist, recently commented that we can expect to see chatbots “proposing strategy and tactics for overcoming business problems.”

Not only will future chatbots be able to help marketing teams craft messages based on the understanding of the language that’s been successful in the past, but they could also develop capabilities to analyze the sentiment of the conversations they’re having, and allocate resources accordingly.

Clearly, AI and NLU-enhanced chatbots are the paths forward for businesses of all sizes that want to build customer relationships and drive results. By leaving behind traditional, rule-based chatbots and embracing new technologies, companies can make their customers feel like their time and problems are valued, gain a comprehensive understanding of consumer needs, increase product purchases, and save on HR costs in the process.

Contributed by DJ Das, Founder and CEO of ThirdEye Data. 

Source: We must move away from ‘cookie-cutter’ chatbots